Using parameter optimization to calibrate a model of user interaction

Simulation models of real-world distributed systems depend both on the accuracy of the underlying model and the interaction between user and system. The user interaction is typically modeled as stochastic process depending on parameters and distributions describing the actual usage. Accurate data is often not available and (manual) assumptions are necessary. Taking an existing large-scale simulation model of the German tolling system we discuss the use of a genetic optimization algorithm for calibrating the simulation model.